Runtimes, orchestration, fleet management and MLOps for the edge
Edge AI software covers inference runtimes, container orchestration for constrained sites, device fleet management, model deployment pipelines and observability.
Solutions in this category
Representative platforms with the details engineering and procurement teams shortlist on.
SUSE (Rancher)
K3s
Apache 2.0 lightweight CNCF-conformant Kubernetes distribution shipped as a single binary under 100 MB.
Specifications are summarised from publicly published manufacturer material and are provided for orientation only. Always confirm current figures directly with the manufacturer before purchasing or designing in.
Companies working in this category
SI
Siemens
Industrial AI company
Industrial automation and software group whose SIMATIC IPC industrial PCs and Industrial Edge platform bring application and AI workloads alongside controllers and plant data.
Provides computer vision tooling including the open-source Inference package and inference server, covering model loading, pre- and post-processing and workflow execution on edge devices.
Edge MLOps platform, now part of Qualcomm, for collecting data, training and deploying models to microcontrollers, NPUs, CPUs and GPUs across a broad partner hardware ecosystem.
Container-based platform for deploying and managing fleets of Linux edge devices, with over-the-air updates, an API and SDK, and support for a large catalogue of device types.
Originator of K3s, the lightweight CNCF-conformant Kubernetes distribution now a CNCF project, widely used to orchestrate containerised AI workloads on edge and Arm hardware.
Maintains the MIT-licensed Ollama runtime for pulling and serving open-weight language and multimodal models locally on workstations, servers and capable edge systems.
Originated ONNX Runtime, the MIT-licensed cross-platform inference accelerator, and publishes the Phi family of small open-weight models aimed at low-latency local inference.
The practice of packaging, deploying, monitoring and updating models across distributed devices, including staged rollouts and rollback when a model regresses in the field.